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• Technical Article •
Application value of intelligent quick magnetic resonance technology in supraspinatus tendon injuries
XIE Xiaoliang  WANG Jianwei  YAN Xiaohui  HE Ling  ZHOU Qiulin  CHEN Cheng  WANG Wei  ZHAO Yi 

Cite this article as: XIE X L, WANG J W, YAN X H, et al. Application value of intelligent quick magnetic resonance technology in supraspinatus tendon injuries[J]. Chin J Magn Reson Imaging, 2024, 15(10): 148-152, 164. DOI:10.12015/issn.1674-8034.2024.10.025.


[Abstract] Objective To explore the application value of Intelligent Quick Magnetic Resonance (IQMR) in the supraspinatus tendon injury.Materials and Methods 40 patients with supraspinatus tendon injuries underwent coronal fast T2WI fat saturation (T2WI-FS) and conventional T2WI-FS sequences scanning of the shoulder, the images of fast T2WI-FS sequence were transferred to IQMR post-processing system to generate T2WI-FSIQMR images automatically. Three groups of MRI images were independently scored by two radiologists for the clarity of lesion detail, the clarity of anatomical structure, overall image artifacts and overall image quality. The degree of supraspinatus tendon injury of three groups of MRI images were independently graded by two radiologists according to Zlatkin classification. The signal-to-noise ratio (SNR) of the supraspinatus, humeral head, and deltoid muscles, contrast noise ratio (CNR1) of the supraspinatus muscle to the humeral head, and contrast noise ratio (CNR2) of deltoid muscle to the humeral head were measured and compared among the three groups of MRI images.Results The scanning time of T2WI-FSIQMR sequence was 41% shorter than that of conventional T2WI-FS sequence. Qualitative analysis: The image quality scores of T2WI-FSIQMR were higher than those of fast T2WI-FS and conventional T2WI-FS in terms of the clarity of lesion detail, the clarity of anatomical structure, overall image artifacts and overall image quality (P<0.001). There was no significant difference among the three groups in the diagnosis of supraspinatus tendon injury (P>0.05). Quantitative analysis: SNR of the supraspinatus, humeral head and deltoid, CNR1 and CNR2 of T2WI-FSIQMR were higher than those of fast T2WI-FS and conventional T2WI-FS (P<0.001).Conclusions In the supraspinatus tendon injury MRI scanning, IQMR technology can significantly reduce the scanning time and improve image quality, which is worthy of clinical application.
[Keywords] supraspinatus muscle；tendon injury；intelligent quick magnetic resonance；signal to noise ratio；contrast to noise ratio；magnetic resonance imaging

XIE Xiaoliang1, 2   WANG Jianwei3   YAN Xiaohui4   HE Ling1, 2   ZHOU Qiulin1, 2   CHEN Cheng1, 2   WANG Wei1, 2   ZHAO Yi1, 2*  

1 Department of Radiology, the Affiliated Hospital of Yangzhou University, Yangzhou 225100, China

2 Yangzhou University, Yangzhou 225100, China

3 Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China

4 Dalian Medical University, Dalian 116000, China

Corresponding author: ZHAO Y, E-mail: zhaoyi8706@163.com

Conflicts of interest   None.

Received  2024-04-09
Accepted  2024-10-10
DOI: 10.12015/issn.1674-8034.2024.10.025
Cite this article as: XIE X L, WANG J W, YAN X H, et al. Application value of intelligent quick magnetic resonance technology in supraspinatus tendon injuries[J]. Chin J Magn Reson Imaging, 2024, 15(10): 148-152, 164. DOI:10.12015/issn.1674-8034.2024.10.025.

[1]
LONGO U G, MAZZOLA A, MAGRÌ F, et al. Histological, radiological and clinical analysis of the supraspinatus tendon and muscle in rotator cuff tears[J/OL]. BMC Musculoskelet Disord, 2023, 24(1): 127 [2024-04-08]. https://bmcmusculoskeletdisord.biomedcentral.com/articles/10.1186/s12891-023-06237-9. DOI: 10.1186/s12891-023-06237-9.
[2]
VIEIRA A C, PÉREZ E M, HERNANDO M F, et al. Myotendinous junction tear of the anterior bundle of the supraspinatus muscle-a rare pattern of injury involving rotator cuff muscles[J/OL]. BJR Case Rep, 2020, 6(4): 20200004 [2024-04-08]. https://academic.oup.com/bjrcr/article/6/4/20200004/7244080?login=false. DOI: 10.1259/bjrcr.20200004.
[3]
UMEHARA J, UEDA Y, YAGI M, et al. Mechanical characteristic of supraspinatus muscle changes independent of its size and intramuscular fat in patient with rotator cuff repair[J/OL]. J Electromyogr Kinesiol, 2023, 73: 102831 [2024-04-08]. https://www.sciencedirect.com/science/article/abs/pii/S1050641123000901?via%3Dihub. DOI: 10.1016/j.jelekin.2023.102831.
[4]
FITZPATRICK L A, ATINGA A, WHITE L, et al. Rotator cuff injury and repair[J]. Semin Musculoskelet Radiol, 2022, 26(5): 585-596. DOI: 10.1055/s-0042-1756167.
[5]
MCCRUM E. MR imaging of the rotator cuff[J]. Magn Reson Imaging Clin N Am, 2020, 28(2): 165-179. DOI: 10.1016/j.mric.2019.12.002.
[6]
JUNIOR J A Y, NICOLAO F A, MATSUNAGA F T, et al. Supraspinatus tears: predictability of magnetic resonance imaging findings based on clinical examination[J]. J Shoulder Elbow Surg, 2021, 30(8): 1834-1843. DOI: 10.1016/j.jse.2021.02.013.
[7]
PATEL M, AMINI M H. Management of acute rotator cuff tears[J]. Orthop Clin North Am, 2022, 53(1): 69-76. DOI: 10.1016/j.ocl.2021.08.003.
[8]
YANG A C, KRETZLER M, SUDARSKI S, et al. Sparse reconstruction techniques in magnetic resonance imaging: methods, applications, and challenges to clinical adoption[J]. Invest Radiol, 2016, 51(6): 349-364. DOI: 10.1097/RLI.0000000000000274.
[9]
XU M, WU Y, LIU J, et al. Application value of intelligent quick magnetic resonance technique in magnetic resonance scanning of cervical vertebra[J]. Chin J Magn Reson Imag, 2023, 14(10): 111-115. DOI: 10.12015/issn.1674-8034.2023.10.019.
[10]
WICAKSONO K P, FUJIMOTO K, FUSHIMI Y, et al. Super-resolution application of generative adversarial network on brain time-of-flight MR angiography: image quality and diagnostic utility evaluation[J]. Eur Radiol, 2023, 33(2): 936-946. DOI: 10.1007/s00330-022-09103-9.
[11]
DO W J, SEO S, HAN Y, et al. Reconstruction of multicontrast MR images through deep learning[J]. Med Phys, 2020, 47(3): 983-997. DOI: 10.1002/mp.14006.
[12]
REN J T, ERIKSEN J G, NIJKAMP J, et al. Comparing different CT, PET and MRI multi-modality image combinations for deep learning-based head and neck tumor segmentation[J]. Acta Oncol, 2021, 60(11): 1399-1406. DOI: 10.1080/0284186X.2021.1949034.
[13]
BIELAK L, WIEDENMANN N, BERLIN A, et al. Convolutional neural networks for head and neck tumor segmentation on 7-channel multiparametric MRI: A leave-one-out analysis[J/OL]. Radiat Oncol, 2020, 15(1): 181 [2024-04-08]. https://ro-journal.biomedcentral.com/articles/10.1186/s13014-020-01618-z. DOI: 10.1186/s13014-020-01618-z.
[14]
HERRMANN J, AFAT S, GASSENMAIER S, et al. Image quality and diagnostic performance of accelerated 2D hip MRI with deep learning reconstruction based on a deep iterative hierarchical network[J/OL]. Diagnostics, 2023, 13(20): 3241 [2024-04-08]. https://www.mdpi.com/2075-4418/13/20/3241. DOI: 10.3390/diagnostics13203241.
[15]
KANEMARU N, TAKAO H, AMEMIYA S, et al. The effect of a post-scan processing denoising system on image quality and morphometric analysis[J]. J Neuroradiol, 2022, 49(2): 205-212. DOI: 10.1016/j.neurad.2021.11.007.
[16]
DRATSCH T, SIEDEK F, ZÄSKE C, et al. Reconstruction of shoulder MRI using deep learning and compressed sensing: a validation study on healthy volunteers[J/OL]. Eur Radiol Exp, 2023, 7(1): 66 [2024-04-08]. https://eurradiolexp.springeropen.com/articles/10.1186/s41747-023-00377-2. DOI: 10.1186/s41747-023-00377-2.
[17]
LEVIN J M, JOHNSON J, TABARESTANI T, et al. Association between supraspinatus tendon retraction, histologic myofiber size, and supraspinatus muscle atrophy on MRI[J]. Am J Sports Med, 2023, 51(8): 1997-2004. DOI: 10.1177/03635465231173697.
[18]
YANG Z H. Technical guidelines for magnetic resonance imaging (2nd Edition)[M].Beijing: China Union Medical College Press, 2023: 428-431.
[19]
JOHNSON P M, LIN D J, ZBONTAR J, et al. Deep learning reconstruction enables prospectively accelerated clinical knee MRI[J/OL]. Radiology, 2023, 307(2): e220425 [2024-04-08]. https://pubs.rsna.org/doi/10.1148/radiol.220425. DOI: 10.1148/radiol.220425.
[20]
WANG Y C, ZHANG X X, HU M C, et al. Value of deep learning reconstruction in optimizing prostate MR T2-weighted imaging scanning time and imaging quality[J]. Chin J Magn Reson Imag, 2023, 14(5): 48-52, 59. DOI: 10.12015/issn.1674-8034.2023.05.010.
[21]
WU Y, XU M, LIU J, et al. The application value of intelligent quick magnetic resonance technology in head MRI scan[J]. J Clin Radiol, 2024, 43(3): 337-340. DOI: 10.3778/j.issn.1673-9418.2004071.
[22]
KIM E, CHO H H, CHO S H, et al. Accelerated synthetic MRI with deep learning-based reconstruction for pediatric neuroimaging[J]. AJNR Am J Neuroradiol, 2022, 43(11): 1653-1659. DOI: 10.3174/ajnr.A7664.
[23]
ZENG G S, GUO Y, ZHAN J Y, et al. A review on deep learning MRI reconstruction without fully sampled k-space[J/OL]. BMC Med Imaging, 2021, 21(1): 195 [2024-04-08]. https://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-021-00727-9. DOI: 10.1186/s12880-021-00727-9.

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